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          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
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            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
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  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Install the neessary packages"
      ],
      "metadata": {
        "id": "hTeOiOtpcrXv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers\n",
        "!pip install datasets\n",
        "!pip install accelerate\n",
        "!pip install peft\n",
        "!pip install bitsandbytes\n",
        "!pip install sentencePiece"
      ],
      "metadata": {
        "id": "mfhCwAuUIdN9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1. Clean the Data \n",
        "1. Please Download the data from the github and save it on your local drive.\n",
        "2. At the end of this step, data should be single JSON file in the following format.\n",
        "```\n",
        "            json_entry = {\n",
        "                'instruction': 'What is diabetes?',\n",
        "                'input': '',\n",
        "                'output': 'Diabetes is ...'\n",
        "            }\n",
        "```\n",
        "3. Save the Json file in your computer."
      ],
      "metadata": {
        "id": "ly2eyDktcyAc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "base_directory='/content/drive/MyDrive/Colab Notebooks/transformer_learn/'"
      ],
      "metadata": {
        "id": "5o5NGaF1eGml"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JSgr2ZoCUMo5"
      },
      "outputs": [],
      "source": [
        "import xmltodict\n",
        "import json\n",
        "import glob\n",
        "import os\n",
        "\n",
        "def convert_xml_to_json(xml_file):\n",
        "    with open(xml_file, 'r', encoding='utf-8') as f:\n",
        "        xml_data = f.read()\n",
        "\n",
        "    xml_dict = xmltodict.parse(xml_data)\n",
        "\n",
        "    # Check if 'Document' key is in the xml_dict and its value is not None\n",
        "    if 'Document' not in xml_dict or xml_dict['Document'] is None or 'QAPairs' not in xml_dict['Document'] or xml_dict['Document']['QAPairs'] is None:\n",
        "        print(f\"Missing or invalid 'Document' or 'QAPairs' key in {xml_file}\")\n",
        "        return []\n",
        "\n",
        "    questions = xml_dict['Document']['QAPairs']['QAPair']\n",
        "\n",
        "    # Ensure questions is a list\n",
        "    if not isinstance(questions, list):\n",
        "        questions = [questions]\n",
        "\n",
        "    json_data = []\n",
        "\n",
        "    for question in questions:\n",
        "        if question['Answer'] and question['Answer'].strip():\n",
        "            json_entry = {\n",
        "                'instruction': question['Question']['#text'],\n",
        "                'input': '',\n",
        "                'output': question['Answer']\n",
        "            }\n",
        "            json_data.append(json_entry)\n",
        "\n",
        "    return json_data\n",
        "\n",
        "\n",
        "# Replace \"files\" with the actual path to your \"files\" directory if needed\n",
        "files_path = base_directory+\"dataset\"\n",
        "\n",
        "# Get all topic directories inside the \"files\" directory\n",
        "topic_directories = [d for d in os.listdir(files_path) if os.path.isdir(os.path.join(files_path, d))]\n",
        "\n",
        "combined_json_data = []\n",
        "\n",
        "for topic_directory in topic_directories:\n",
        "    # Get all XML files inside the topic directory\n",
        "    xml_files = glob.glob(os.path.join(files_path, topic_directory, \"*.xml\"))\n",
        "\n",
        "    for xml_file in xml_files:\n",
        "        combined_json_data.extend(convert_xml_to_json(xml_file))\n",
        "\n",
        "# Save combined JSON data into a single file\n",
        "with open(base_directory+ 'alpaca_data.json', 'w', encoding='utf-8') as f:\n",
        "    json.dump(combined_json_data, f, indent=4)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#2. Declare the Model and Tokenizer\n",
        "We will utilize the lama-7b-hf model created by Meta. To obtain the model weights from Meta, you must submit a request through https://ai.facebook.com/blog/large-language-model-llama-meta-ai/. However, the Llama model's weights were inadvertently leaked and incorporated into Hugging Face's decapoda-research/llama-7b-hf. As a result, we will employ the Llama model from decapoda-research rather than requesting the weights from Meta and waiting."
      ],
      "metadata": {
        "id": "GNvQshQze-Kw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import LlamaTokenizer, LlamaForCausalLM\n",
        "\n",
        "BASE_MODEL = \"decapoda-research/llama-7b-hf\"\n",
        " \n",
        "model = LlamaForCausalLM.from_pretrained(\n",
        "    BASE_MODEL,\n",
        "    load_in_8bit=True,\n",
        "    device_map=\"auto\",\n",
        ")\n",
        " \n",
        "tokenizer = LlamaTokenizer.from_pretrained(BASE_MODEL)\n",
        " \n",
        "tokenizer.pad_token_id = 0\n",
        "tokenizer.padding_side = \"left\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676,
          "referenced_widgets": [
            "50c743ff382a43f59aeddf73b4087145",
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        "id": "C3d4njzYUtWx",
        "outputId": "aee58180-6f69-495a-e714-233895726f79"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Overriding torch_dtype=None with `torch_dtype=torch.float16` due to requirements of `bitsandbytes` to enable model loading in mixed int8. Either pass torch_dtype=torch.float16 or don't pass this argument at all to remove this warning.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "===================================BUG REPORT===================================\n",
            "Welcome to bitsandbytes. For bug reports, please run\n",
            "\n",
            "python -m bitsandbytes\n",
            "\n",
            " and submit this information together with your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
            "================================================================================\n",
            "bin /usr/local/lib/python3.9/dist-packages/bitsandbytes/libbitsandbytes_cuda118.so\n",
            "CUDA_SETUP: WARNING! libcudart.so not found in any environmental path. Searching in backup paths...\n",
            "CUDA SETUP: CUDA runtime path found: /usr/local/cuda/lib64/libcudart.so\n",
            "CUDA SETUP: Highest compute capability among GPUs detected: 8.0\n",
            "CUDA SETUP: Detected CUDA version 118\n",
            "CUDA SETUP: Loading binary /usr/local/lib/python3.9/dist-packages/bitsandbytes/libbitsandbytes_cuda118.so...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: /usr/lib64-nvidia did not contain ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] as expected! Searching further paths...\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/sys/fs/cgroup/memory.events /var/colab/cgroup/jupyter-children/memory.events')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('8013'), PosixPath('//172.28.0.1'), PosixPath('http')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('--logtostderr --listen_host=172.28.0.12 --target_host=172.28.0.12 --tunnel_background_save_url=https'), PosixPath('//colab.research.google.com/tun/m/cc48301118ce562b961b3c22d803539adc1e0c19/gpu-a100-s-3l6f1lb8tisdc --tunnel_background_save_delay=10s --tunnel_periodic_background_save_frequency=30m0s --enable_output_coalescing=true --output_coalescing_required=true')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/env/python')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('//ipykernel.pylab.backend_inline'), PosixPath('module')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: Found duplicate ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] files: {PosixPath('/usr/local/cuda/lib64/libcudart.so'), PosixPath('/usr/local/cuda/lib64/libcudart.so.11.0')}.. We'll flip a coin and try one of these, in order to fail forward.\n",
            "Either way, this might cause trouble in the future:\n",
            "If you get `CUDA error: invalid device function` errors, the above might be the cause and the solution is to make sure only one ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] in the paths that we search based on your env.\n",
            "  warn(msg)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/33 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The tokenizer class you load from this checkpoint is not the same type as the class this function is called from. It may result in unexpected tokenization. \n",
            "The tokenizer class you load from this checkpoint is 'LLaMATokenizer'. \n",
            "The class this function is called from is 'LlamaTokenizer'.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#3. Data Preprocessing\n",
        "1. We allocate 90% of the data for training and 10% for validation purposes.\n",
        "2. The generate_prompt function establishes the prompt format. Reference: https://github.com/tloen/alpaca-lora\n",
        "  * Here, Instruction ==> Question, Input ==> Context, Output ==> Answer\n",
        "  * If there is context, the prompt will have three keys: [Instruction, Input,Output ]\n",
        "  * If there is no context, the prompt will have two keys: [Instruction,Output ]\n",
        "3. We create both training and validation datasets.\n",
        "4. Initially, we generate a prompt and subsequently tokenize it.\n",
        "5. The training process requires input_ids and attention_mask. It is not necessary to explicitly define the label.\n",
        "6. This step should produce training and validation dataset with format:\n",
        "```\n",
        "Dataset({\n",
        "    features: ['instruction', 'input', 'output', 'input_ids', 'attention_mask'],\n",
        "    num_rows: 14762\n",
        "})\n",
        "```"
      ],
      "metadata": {
        "id": "bwsOkJbUl7uV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer, TrainingArguments\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "train_data = load_dataset(\"json\", data_files=base_directory+\"alpaca_data.json\", split=\"train[:90%]\")\n",
        "valid_data = load_dataset(\"json\", data_files=base_directory+\"alpaca_data.json\", split=\"train[90%:]\")\n",
        "\n",
        "def generate_prompt(data_point):\n",
        "    if data_point[\"input\"]:\n",
        "        return f\"\"\"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n",
        "\n",
        "### Instruction:\n",
        "{data_point[\"instruction\"]}\n",
        "\n",
        "### Input:\n",
        "{data_point[\"input\"]}\n",
        "\n",
        "### Response:\n",
        "{data_point[\"output\"]}\"\"\"\n",
        "    else:\n",
        "        return f\"\"\"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n",
        "\n",
        "### Instruction:\n",
        "{data_point[\"instruction\"]}\n",
        "\n",
        "### Response:\n",
        "{data_point[\"output\"]}\"\"\"\n",
        "\n",
        "train_data1 = load_dataset(\"json\", data_files=base_directory+\"alpaca_data.json\", split=\"train[:90%]\")\n",
        "valid_data2 = load_dataset(\"json\", data_files=base_directory+\"alpaca_data.json\", split=\"train[90%:]\")\n",
        "data_train = train_data1.shuffle().map(\n",
        "    lambda data_point: tokenizer(\n",
        "        generate_prompt(data_point),\n",
        "        truncation=True,\n",
        "        max_length=1000,\n",
        "        padding=\"max_length\",\n",
        "    )\n",
        ")\n",
        "data_valid = valid_data2.shuffle().map(\n",
        "    lambda data_point: tokenizer(\n",
        "        generate_prompt(data_point),\n",
        "        truncation=True,\n",
        "        max_length=1000,\n",
        "        padding=\"max_length\",\n",
        "    )\n",
        ")\n",
        "\n"
      ],
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        "id": "1hs4RrIqUuSl",
        "outputId": "db30e3ac-86c0-4480-d08a-883ddac1446e"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset json/default to /root/.cache/huggingface/datasets/json/default-0b9353c21df78eea/0.0.0/fe5dd6ea2639a6df622901539cb550cf8797e5a6b2dd7af1cf934bed8e233e6e...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6d3835a965e442818162380309dfa5aa"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b49bdfdd37cd414a8c6c7c255fe163f4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6223e47e86d747bc8585f350c520201a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset json downloaded and prepared to /root/.cache/huggingface/datasets/json/default-0b9353c21df78eea/0.0.0/fe5dd6ea2639a6df622901539cb550cf8797e5a6b2dd7af1cf934bed8e233e6e. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:Found cached dataset json (/root/.cache/huggingface/datasets/json/default-0b9353c21df78eea/0.0.0/fe5dd6ea2639a6df622901539cb550cf8797e5a6b2dd7af1cf934bed8e233e6e)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/14762 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7971bc2e880440e2b923cf8ddc3e3d7b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1640 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f2cc31a0aea946a6bcfc9d3ce6338382"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "z5UTh4qxcrWu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Training With PEFT"
      ],
      "metadata": {
        "id": "bxsbKofncxmP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Delaring Lora Variables"
      ],
      "metadata": {
        "id": "XwyAbSCeiKha"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "LORA_R = 8 #lora dimension\n",
        "LORA_ALPHA = 16 #(`float`): The alpha parameter for Lora scaling\n",
        "LORA_DROPOUT= 0.05\n",
        "# this defines what parameters need to be trained\n",
        "LORA_TARGET_MODULES = [\n",
        "    \"q_proj\",\n",
        "    \"v_proj\",\n",
        "]\n",
        " \n",
        "BATCH_SIZE = 128\n",
        "MICRO_BATCH_SIZE = 4\n",
        "GRADIENT_ACCUMULATION_STEPS = BATCH_SIZE // MICRO_BATCH_SIZE\n",
        "LEARNING_RATE = 4e-4\n",
        "TRAIN_STEPS = 50\n",
        "OUTPUT_DIR = base_directory"
      ],
      "metadata": {
        "id": "2xsaf8nnWYgr"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The Below output shows that we are only training 0.06 percentage of parameter, which will higly spped-up fine-tunning process"
      ],
      "metadata": {
        "id": "2tL7mENziPkQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from peft import LoraConfig, get_peft_model, get_peft_model_state_dict, prepare_model_for_int8_training\n",
        "\n",
        "model = prepare_model_for_int8_training(model)\n",
        "config = LoraConfig(\n",
        "    r=LORA_R,\n",
        "    lora_alpha=LORA_ALPHA,\n",
        "    target_modules=LORA_TARGET_MODULES,\n",
        "    lora_dropout=LORA_DROPOUT,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\",\n",
        ")\n",
        "model = get_peft_model(model, config)\n",
        "model.print_trainable_parameters()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PMF49CETWvy3",
        "outputId": "6f238fcd-8555-44e3-f066-9d8ff792abf1"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainable params: 4194304 || all params: 6742609920 || trainable%: 0.06220594176090199\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import transformers\n",
        "training_arguments = transformers.TrainingArguments(\n",
        "    per_device_train_batch_size=MICRO_BATCH_SIZE,\n",
        "    gradient_accumulation_steps=GRADIENT_ACCUMULATION_STEPS,\n",
        "    warmup_steps=10,\n",
        "    max_steps=TRAIN_STEPS,\n",
        "    learning_rate=LEARNING_RATE,\n",
        "    fp16=True,\n",
        "    logging_steps=10,\n",
        "    optim=\"adamw_torch\",\n",
        "    evaluation_strategy=\"steps\",\n",
        "    save_strategy=\"steps\",\n",
        "    eval_steps=50,\n",
        "    save_steps=50,\n",
        "    output_dir=OUTPUT_DIR,\n",
        "    save_total_limit=3,\n",
        "    load_best_model_at_end=True,\n",
        "    report_to=\"tensorboard\"\n",
        ")"
      ],
      "metadata": {
        "id": "obpG5oepWyfF"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Train and save the chatbot"
      ],
      "metadata": {
        "id": "ncfykPuwijY7"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "trainer = transformers.Trainer(\n",
        "    model=model,\n",
        "    train_dataset=data_train,\n",
        "    eval_dataset=data_valid,\n",
        "    args=training_arguments,\n",
        "    data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),\n",
        ")\n",
        "model.config.use_cache = False\n",
        "trainer.train(resume_from_checkpoint=False)\n",
        "\n",
        "model.save_pretrained(base_directory+\"chatbot\")"
      ],
      "metadata": {
        "id": "O1vQL6okYADe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "One crucial aspect of PET is that it only stores the newly trained parameters, rather than the entire model. As a result, the size of our chatbot remains relatively small, amounting to just a few megabytes."
      ],
      "metadata": {
        "id": "PpJqUeZ8i0L_"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "NXhZCBVzI8ua"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Loading Model for inference"
      ],
      "metadata": {
        "id": "SaJtSzNDjJoQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Process "
      ],
      "metadata": {
        "id": "-n_WgsLsiyEI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModel\n",
        "from peft import PeftModel, PeftConfig\n",
        "import torch\n",
        "from transformers import LlamaTokenizer, LlamaForCausalLM\n",
        "\n",
        "\n",
        "base_directory='/Users/premtimsina/Documents/bpbbook/chapter2_huggingFace/'\n",
        "base_directory='/content/drive/MyDrive/Colab Notebooks/transformer_learn/'\n",
        "BASE_MODEL = \"decapoda-research/llama-7b-hf\"\n",
        "\n",
        "# this is PEFT model specific to chatbot \n",
        "peft_model_id = base_directory+\"chatbot\"\n",
        "config = PeftConfig.from_pretrained(peft_model_id)\n",
        "# this is base model which is llama-7b-hf\n",
        "model = LlamaForCausalLM.from_pretrained(BASE_MODEL)\n",
        "\n",
        "# we are creating entire model combining base model and peft model\n",
        "model = PeftModel.from_pretrained(model, peft_model_id)\n",
        "tokenizer = LlamaTokenizer.from_pretrained(BASE_MODEL)\n",
        "\n",
        "model = model.to(\"cuda\")\n",
        "model.eval()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "f0112de6eb124a85a6e3193882a4b8f1",
            "726dc5394a754bff9f79fe17a29d6a40",
            "d6d3667166994f56a0e544ac5b62ed4c",
            "9bc603984db846499a69b83bcebe9dd8",
            "f5c63eeb5dda45148e39f619f37b5251",
            "106a8d732f8d48048b39e341df0481b8",
            "af688b843336422ca71ee97d7d87ac9a",
            "b34efea064ad473fa437beaa8dfdc747",
            "0363ea9c3c284abb802596136158d6a4",
            "50eee419530945aa974565f78ae23b8e",
            "fb9e274bd62f427c9739e516ebafe331"
          ]
        },
        "id": "7bKUi0NIZ0U2",
        "outputId": "71a370f2-636f-4587-fc3f-1eb01eae8eb4"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "===================================BUG REPORT===================================\n",
            "Welcome to bitsandbytes. For bug reports, please run\n",
            "\n",
            "python -m bitsandbytes\n",
            "\n",
            " and submit this information together with your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
            "================================================================================\n",
            "bin /usr/local/lib/python3.9/dist-packages/bitsandbytes/libbitsandbytes_cuda118.so\n",
            "CUDA_SETUP: WARNING! libcudart.so not found in any environmental path. Searching in backup paths...\n",
            "CUDA SETUP: CUDA runtime path found: /usr/local/cuda/lib64/libcudart.so\n",
            "CUDA SETUP: Highest compute capability among GPUs detected: 8.0\n",
            "CUDA SETUP: Detected CUDA version 118\n",
            "CUDA SETUP: Loading binary /usr/local/lib/python3.9/dist-packages/bitsandbytes/libbitsandbytes_cuda118.so...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: /usr/lib64-nvidia did not contain ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] as expected! Searching further paths...\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/sys/fs/cgroup/memory.events /var/colab/cgroup/jupyter-children/memory.events')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('8013'), PosixPath('http'), PosixPath('//172.28.0.1')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('--logtostderr --listen_host=172.28.0.12 --target_host=172.28.0.12 --tunnel_background_save_url=https'), PosixPath('//colab.research.google.com/tun/m/cc48301118ce562b961b3c22d803539adc1e0c19/gpu-a100-s-3l6f1lb8tisdc --tunnel_background_save_delay=10s --tunnel_periodic_background_save_frequency=30m0s --enable_output_coalescing=true --output_coalescing_required=true')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/env/python')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('module'), PosixPath('//ipykernel.pylab.backend_inline')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: Found duplicate ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] files: {PosixPath('/usr/local/cuda/lib64/libcudart.so'), PosixPath('/usr/local/cuda/lib64/libcudart.so.11.0')}.. We'll flip a coin and try one of these, in order to fail forward.\n",
            "Either way, this might cause trouble in the future:\n",
            "If you get `CUDA error: invalid device function` errors, the above might be the cause and the solution is to make sure only one ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] in the paths that we search based on your env.\n",
            "  warn(msg)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/33 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f0112de6eb124a85a6e3193882a4b8f1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The tokenizer class you load from this checkpoint is not the same type as the class this function is called from. It may result in unexpected tokenization. \n",
            "The tokenizer class you load from this checkpoint is 'LLaMATokenizer'. \n",
            "The class this function is called from is 'LlamaTokenizer'.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PeftModelForCausalLM(\n",
              "  (base_model): LoraModel(\n",
              "    (model): LlamaForCausalLM(\n",
              "      (model): LlamaModel(\n",
              "        (embed_tokens): Embedding(32000, 4096, padding_idx=31999)\n",
              "        (layers): ModuleList(\n",
              "          (0-31): 32 x LlamaDecoderLayer(\n",
              "            (self_attn): LlamaAttention(\n",
              "              (q_proj): Linear(\n",
              "                in_features=4096, out_features=4096, bias=False\n",
              "                (lora_dropout): Dropout(p=0.05, inplace=False)\n",
              "                (lora_A): Linear(in_features=4096, out_features=8, bias=False)\n",
              "                (lora_B): Linear(in_features=8, out_features=4096, bias=False)\n",
              "              )\n",
              "              (k_proj): Linear(in_features=4096, out_features=4096, bias=False)\n",
              "              (v_proj): Linear(\n",
              "                in_features=4096, out_features=4096, bias=False\n",
              "                (lora_dropout): Dropout(p=0.05, inplace=False)\n",
              "                (lora_A): Linear(in_features=4096, out_features=8, bias=False)\n",
              "                (lora_B): Linear(in_features=8, out_features=4096, bias=False)\n",
              "              )\n",
              "              (o_proj): Linear(in_features=4096, out_features=4096, bias=False)\n",
              "              (rotary_emb): LlamaRotaryEmbedding()\n",
              "            )\n",
              "            (mlp): LlamaMLP(\n",
              "              (gate_proj): Linear(in_features=4096, out_features=11008, bias=False)\n",
              "              (down_proj): Linear(in_features=11008, out_features=4096, bias=False)\n",
              "              (up_proj): Linear(in_features=4096, out_features=11008, bias=False)\n",
              "              (act_fn): SiLUActivation()\n",
              "            )\n",
              "            (input_layernorm): LlamaRMSNorm()\n",
              "            (post_attention_layernorm): LlamaRMSNorm()\n",
              "          )\n",
              "        )\n",
              "        (norm): LlamaRMSNorm()\n",
              "      )\n",
              "      (lm_head): Linear(in_features=4096, out_features=32000, bias=False)\n",
              "    )\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Inference Function"
      ],
      "metadata": {
        "id": "9VThxygtjhFm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "DEVICE='cuda'"
      ],
      "metadata": {
        "id": "Hu0uZ9BKXjt7"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import textwrap\n",
        "from peft import PeftModel\n",
        "from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig\n",
        "from transformers.generation.utils import GreedySearchDecoderOnlyOutput\n",
        "\n",
        "def ask_ai_doctor(instruction: str, model: PeftModel) -> str:\n",
        "    PROMPT_TEMPLATE = f\"\"\"\n",
        "    Below is an instruction that describes a task. Write a response that appropriately completes the request.\n",
        "     \n",
        "    ### Instruction:\n",
        "    [INSTRUCTION]\n",
        "     \n",
        "    ### Response:\n",
        "    \"\"\"\n",
        "    \n",
        "    # Replace [INSTRUCTION] in the template with the given instruction\n",
        "    prompt = PROMPT_TEMPLATE.replace(\"[INSTRUCTION]\", instruction)\n",
        "    \n",
        "    # Generate an encoded input tensor from the prompt\n",
        "    encoding = tokenizer(prompt, return_tensors=\"pt\")\n",
        "    input_ids = encoding[\"input_ids\"].to(DEVICE)\n",
        "\n",
        "    # Set generation configuration\n",
        "    '''\n",
        "    controls various aspects of the text generation process.\n",
        "    temperature: This parameter (set to 0.1) controls the randomness of the generated text. lower value more determenistic; higher value more random\n",
        "    top_p: This parameter (set to 0.75) is also called nucleus sampling. In our case, the model will only consider tokens that make up the top 75% of probabilities for the next word\n",
        "    repetition_penalty: This parameter (set to 1.1) is used to penalize repetitions in the generated text. A value greater than 1 helps to reduce the frequency of repeated phrases\n",
        "    '''\n",
        "    generation_config = GenerationConfig(\n",
        "        temperature=0.1,\n",
        "        top_p=0.75,\n",
        "        repetition_penalty=1.1,\n",
        "    )\n",
        "    \n",
        "    # Generate the response using the model and the configuration\n",
        "\n",
        "    with torch.inference_mode():\n",
        "        response = model.generate(\n",
        "            input_ids=input_ids,\n",
        "            generation_config=generation_config,\n",
        "            return_dict_in_generate=True,\n",
        "            output_scores=True,\n",
        "            max_new_tokens=250,\n",
        "        )\n",
        "    \n",
        "    # Decode the response and extract the relevant part\n",
        "    decoded_output = tokenizer.decode(response.sequences[0])\n",
        "    formatted_response = decoded_output.split(\"### Response:\")[1].strip()\n",
        "    \n",
        "    # Wrap and return the formatted response\n",
        "    return \"\\n\".join(textwrap.wrap(formatted_response))\n"
      ],
      "metadata": {
        "id": "aBBLVWJAfK1p"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(ask_ai_doctor('What are symptoms of Cirrhosis?', model))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0gwNayFWI_eE",
        "outputId": "8d337b53-eca3-48d3-d523-8fc9a16b5d9a"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The following list of signs and symptoms may be associated with\n",
            "cirrhosis.  Some people with cirrhosis do not have any of these\n",
            "symptoms.   If you are concerned about how your general health is\n",
            "affected by cirrhosis, talk to your doctor or nurse practitioner.\n",
            "Signs and Symptoms of Cirrhosis   ------------------------   Abdominal\n",
            "swelling (ascites)   Bleeding problems   Blurred vision   Breath odor\n",
            "Confusion   Constipation   Difficulty concentrating   Dizziness\n",
            "Fatigue   Fluid retention   Gallstones   Gout   Hair loss   Headache\n",
            "Itching   Jaundice   Liver cancer   Memory loss   Muscle weakness\n",
            "Nausea   Neuropathy   Night sweats   Pain in the upper right abdomen\n",
            "Poor appetite   Skin itching   Sleepiness   Stomach pain   Swollen\n",
            "legs and feet   Tiredness   Weight gain   Yellow skin and eyes\n",
            "------------------------   How common are these symptoms?   These\n",
            "symptoms can occur at different times\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login"
      ],
      "metadata": {
        "id": "oujRqT9IXDFu"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "notebook_login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "eb1ce7e0246a44f4901b8d210d1637bf",
            "22c3444491cd46c2b90c10ae51617e2b",
            "e2a9a588928948ea80f8edd67de5c5b3",
            "5ef68a4243bc48788ac4fafaeb2b06f9",
            "be46b5f6504a4690a1ca349b925f994e",
            "b48afc0b489946248cf40968060dbc58",
            "7c12e18930e64815aa6abde50b62b933",
            "6198c07cfb68472a9eb44d64f12ecec4",
            "069acec41bd04bb7a77cf63928d1bd8b",
            "a68baafded5a4f7a8f729b30b92bf025",
            "48356b00d03541abbd13e67a730870ff",
            "2ec7fbc6fbda4259a02a8876402560c5",
            "962566eca9f6488890fd54958b255efe",
            "3d7c6b25113b4f7e85bb968d7f5720d9",
            "84977d4c8a2d44e980eb9878e8110ae3",
            "8badd5e2a7eb46be8bea6671ba18076c",
            "93a64672268542649288fb4b4f07c5bb",
            "d877d24fb8f14182811ccc33415a4fbf",
            "da7670d7cb7b46e8b043411d6b3b0241",
            "9dc0b7bab53d4e36bb3d04b1d4bb82bd",
            "d39410a55b5742779daecd85321b532e",
            "46dc52d4704240d3aa0d30d2463913e6",
            "b3c45ad692c148d493f080c746c3eb1c",
            "bae94ba76e3c4ec08f41339a4006c164",
            "e12e1768d62a4f1b993c60dd57f080f3",
            "ab1154f47b994696bd3725b14476038d",
            "d2d553c73df140e5b0db0533812809f8",
            "9aeb40bd4dfd4c318769ddb2041f2c3d",
            "e49f3872fffa4528984e2eff3a5a1628",
            "0f6778469f434cad9b12deb936feb7d4",
            "8bdee943d5ac4bb4bdb227ccba504666",
            "27481e5242da401abf36cce1b9dd4b08"
          ]
        },
        "id": "ztb31ixajI3P",
        "outputId": "3349d977-d0f9-4464-cee6-de1c121da801"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "eb1ce7e0246a44f4901b8d210d1637bf"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.push_to_hub(\"prem-timsina/alpaca-ai-doctor\", use_auth_token=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 135,
          "referenced_widgets": [
            "8797e5ab6f034daa858807c33fc239be",
            "e4820ccf98344bf380844ec7ebad199a",
            "36f25b3f469c49a88aed627363bf4f34",
            "0289724256af414d95b4ad154dfbc0f8",
            "34e5115eb16c4b828870d947f4301254",
            "b370206610ee4b32a98fbb55954347bf",
            "ddd70b24cccf4d04b600ec5ded701916",
            "cbb3d68654bb4688b9836e627c45f3d2",
            "a34a9315491943f38738a277ba395368",
            "9652fa8f96ec4730b673bc362b46905a",
            "b306cffd5fa44594b1e495ee5bc4d27a",
            "21f9de5110314e44a35b4cd80ddad7c9",
            "c7f41f6526b94702af10a47ee48984c9",
            "8450d8fdf7674647a499065c9d37c00f",
            "c41fcb80ea2649059396946108e194c1",
            "da2fdb74b9644c35af98abdb422713b0",
            "5e54c1a819ed497f82d4de16bdd1497d",
            "40196715379b4e269ccca4e5e120af1b",
            "5c47009656ad40dc9ff7e9bd5e5c2d16",
            "456c1763363843b3b4e793e69b429e70",
            "b41ed183871a4ddda1bd0285b3511d0c",
            "894fcbbd987c4a448b9ef31b41f6371b"
          ]
        },
        "id": "CZ2QrGREjOIs",
        "outputId": "94a723da-ea81-4887-dfa6-76a9557c998f"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Upload 1 LFS files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8797e5ab6f034daa858807c33fc239be"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "adapter_model.bin:   0%|          | 0.00/16.8M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "21f9de5110314e44a35b4cd80ddad7c9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/prem-timsina/alpaca-ai-doctor/commit/be92fbce9e96a94dc5e8d3cbaed190acc7c03462', commit_message='Upload model', commit_description='', oid='be92fbce9e96a94dc5e8d3cbaed190acc7c03462', pr_url=None, pr_revision=None, pr_num=None)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fD7uKU3rrNM9"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}